mlsanityData to 7 Oct 2026

Effective Image Tampering Localization with Multi-Scale ConvNeXt Feature Fusion

arXiv 2208.13739 ↗First posted 29 Aug 2022cs.CVcited by 4

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Abstract (arXiv)

With the widespread use of powerful image editing tools, image tampering becomes easy and realistic. Existing image forensic methods still face challenges of low generalization performance and robustness. In this letter, we propose an effective image tampering localization scheme based on ConvNeXt network and multi-scale feature fusion. Stacked ConvNeXt blocks are used as an encoder to capture hierarchical multi-scale features, which are then fused in decoder for locating tampered pixels accurately. Combined loss and effective data augmentation are adopted to further improve the model performance. Extensive experimental results show that localization performance of our proposed scheme outperforms other state-of-the-art ones. The source code will be available at https://github.com/ZhuHC98/ITL-SSN.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 15 of its 33 references that are in the map. First posted on arXiv 29 Aug 2022.

Builds on

15 of 33 references.

References · 15

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  9. 2021PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localizationcited by 133
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3 more
  1. 2019Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeriescited by 59
  2. 2019Forensic Similarity for Digital Imagescited by 27
  3. 2021Self-Adversarial Training incorporating Forgery Attention for Image Forgery Localizationcited by 24

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Cited by year

Citing papers per year: 2023: 3; 2024: 1.

4 papers.

  1. 2023Effective Image Tampering Localization via Enhanced Transformer and Co-attention Fusioncited by 15
  2. 2024Exploring Multi-view Pixel Contrast for General and Robust Image Forgery Localizationcited by 15
  3. 2023Progressive Feedback-Enhanced Transformer for Image Forgery Localizationcited by 4
  4. 2023Spatio-temporal Co-attention Fusion Network for Video Splicing Localizationcited by 1